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Hyperspectral Image Classification

Hyperspectral Image Classification is a task in the field of remote sensing and computer vision. It involves the classification of pixels in hyperspectral images into different classes based on their spectral signature. Hyperspectral images contain information about the reflectance of objects in hundreds of narrow, contiguous wavelength bands, making them useful for a wide range of applications, including mineral mapping, vegetation analysis, and urban land-use mapping. The goal of this task is to accurately identify and classify different types of objects in the image, such as soil, vegetation, water, and buildings, based on their spectral properties.

( Image credit: Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification )

Papers

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Showing 276–286 of 286 papers

TitleStatusHype
Hyperspectral Image Classification and Clutter Detection via Multiple Structural Embeddings and Dimension Reductions—0
Robust hyperspectral image classification with rejection fields—0
Kernel Task-Driven Dictionary Learning for Hyperspectral Image Classification—0
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints—0
Fast forward feature selection for the nonlinear classification of hyperspectral imagesCode0
HyperSpectral classification with adaptively weighted L1-norm regularization and spatial postprocessing—0
Structured Priors for Sparse-Representation-Based Hyperspectral Image Classification—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering—0
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification—0
Feature Selection and Classification of Hyperspectral Images With Support Vector Machines—0
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